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Nodexl – Social Network Analysis in Excel

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Nodexl – Social Network Analysis in Excel Natasa Milic-Frayling, Microsoft Research Ben Shneiderman, Univ. of Maryland Marc A. Smith, Connected Action • Input devices & strategies • Keyboards, pointing devices, voice • Direct manipulation • Menus, forms, commands • Output devices & formats • Screens, windows, color, sound • Text, tables, graphics • Instructions, messages, help • Collaboration & Social Media • Help, tutorials, training • Search www.awl.com/DTUI • Visualization Fifth Edition: 2010 1) E-commerce & National Priorities • Customer loyalty, community formation • Disaster response, community safety • Health, energy, education, e-government 2) Develop Theories of Social Participation • How do social media networks evolve? • How can participation be increased? 3) Provide Technology Infrastructure • Scalable, reliable, universal, manageable • Protect privacy, stop attacks, resolve conflicts 1) E-commerce & National Priorities • Customer loyalty, community formation • Disaster response, community safety • Health, energy, education, e-government 2) Develop Theories of Social Participation • How do social media networks evolve? • How can participation be increased? 3) Provide Technology Infrastructure • Scalable, reliable, universal, manageable • Protect privacy, stop attacks, resolve conflicts Informal Gathering College Park, MD, April 2009 Article: Science March 2009 BEN SHNEIDERMAN http://iparticipate.wikispaces.com NSF Workshops: Palo Alto & DC www.tmsp.umd.edu Community Informatics Research Network intlsocialparticipation.net E-Commerce Social Media 911.gov • Residents report information • Professionals disseminate instructions • Resident-to-Resident assistance Sending SMS message to 911, includes your phone number, location and time Shneiderman & Preece, Science (Feb. 16, 2007) www.cs.umd.edu/hcil/911gov 911.gov Amber Alert • Residents report information • Professionals disseminate instructions • Resident-to-Resident assistance Sending SMS message to 911, includes your phone number, location and time Shneiderman & Preece, Science (Feb. 16, 2007) www.ncmec.org www.cs.umd.edu/hcil/911gov www.missingkids.com 911.gov Amber Alert • Residents report information • Professionals disseminate instructions • Resident-to-Resident assistance Sending SMS message to 911, includes your phone number, location and time Shneiderman & Preece, Science (Feb. 16, 2007) www.cs.umd.edu/hcil/911gov Health, Energy, Education,… Health, Energy, Education,… Health, Energy, Education,… 1) E-commerce & National Priorities • Customer loyalty, community formation • Disaster response, community safety • Health, energy, education, e-government 2) Develop Theories of Social Participation • How do social media networks evolve? • How can participation be increased? 3) Provide Technology Infrastructure • Scalable, reliable, universal, manageable • Protect privacy, stop attacks, resolve conflicts Network Theories: Evolution models • Random, preferential attachment,… • Monotonic, bursty,… • Power law for degree (hubs & indexes) • Small-world property • Forest fire, spreading activation,… • Matures, decays, fragments, … Watts & Strogatz, Nature 1998; Barabasi, Science 1999, 2009; Newman, Phys. Rev. Letters 2002 Kumar, Novak & Tomkins, KDD2006 Leskovec, Faloutsos & Kleinberg, TKDD2007 Network Theories: Social science • Relationships & roles • Strong & weak ties • Motivations: egoism, altruism, collectivism, principlism • Collective intelligence & action • Leadership & governance • Social information foraging Moreno, 1938; Granovetter, 1971; Burt, 1987; Ostrom, 1992; Wellman, 1993; Batson, Ahmad & Tseng, 2002; Malone, Laubaucher & Dellarocas, 2009; Pirolli, 2009 Network Theories: Stages of participation Wikipedia, Discussion & Reporting • Reader • First-time Contributor (Legitimate Peripheral Participation) • Returning Contributor • Frequent Contributor Preece, Nonnecke & Andrews, CHB2004 Forte & Bruckman, SIGGROUP2005; Hanson, 2008 Porter: Designing for the Social Web, 2008 Vassileva, 2002, 2005; Ling et al., JCMC 2005; Rashid et al., CHI2006 From Reader to Leader: Motivating Technology-Mediated Social Participation All Contributor Collaborator ` Leader Users Reader Preece & Shneiderman, AIS Trans. Human-Computer Interaction1 (1), 2009 aisel.aisnet.org/thci/vol1/iss1/5/ 1) E-commerce & National Priorities • Customer loyalty, community formation • Disaster response, community safety • Health, energy, education, e-government 2) Develop Theories of Social Participation • How do social media networks evolve? • How can participation be increased? 3) Provide Technology Infrastructure • Scalable, reliable, universal, manageable • Protect privacy, stop attacks, resolve conflicts • Mobile, Desktop, Web, Cloud • 100% uptime, 100% secure • Giga-collabs, Tera-contribs • Universal accessibility & usability • Trust, empathy, responsibility, privacy • Leaders can manage usage • Designers can continuously improve Footprints of Human Activity • Footprints in sand as Caesarea Preparation • Own the problem & define the schedule • Data cleaning & conditioning • Handle missing & uncertain data • Extract subsets & link to related information • Integrates statistics & visualization • 4 case studies, 4-8 weeks (journalist, bibliometrician, terrorist analyst & organizational analyst) • Identified desired features, gave strong positive feedback about benefits of integration www.cs.umd.edu/hcil/socialaction Perer & Shneiderman, CHI2008, IEEE CG&A 2009 http://www.youtube.com/watch?v=0M3T65Iw3Ac www.codeplex.com/nodexl www.codeplex.com/nodexl www.codeplex.com/nodexl https://wiki.cs.umd.edu/cmsc734_09/index.php?title=Homework_Number_3 I. Getting Started with Analyzing Social Media Networks 1. Introduction to Social Media and Social Networks 2. Social media: New Technologies of Collaboration 3. Social Network Analysis II. NodeXL Tutorial: Learning by Doing 4. Layout, Visual Design & Labeling 5. Calculating & Visualizing Network Metrics 6. Preparing Data & Filtering 7. Clustering &Grouping III Social Media Network Analysis Case Studies 8. Email 9. Threaded Networks 10. Twitter 11. Facebook 12. WWW 13. Flickr 14. YouTube 15. Wiki Networks www.elsevier.com/wps/find/bookdescription.cws_home/723354/description Challenge: Requires Partitioning • Easy : Only need locally connected vertices e.g Vertex Degree, Eigenvector centrality • Relatively Hard : Need local & some global graph knowledge e.g. Fruchterman-Reingold layout • Hard : Need global graph knowledge at each node e.g. all pairs shortest paths -> betweenness centrality Udayan Khurana Implement and Measure Performance for Fruchterman-Reingold Layout Algorithm GPU GeForce GTX 285, 1476 MHz, 240 cores Host CPU 3 GHz, Intel(R) Core(TM)2 Duo CUDA Graph Name #Nodes #Edges F-R run time F-R run time (seconds) (seconds) CA-AstroPh 18,772 396,160 84 1 cit-HepPh 34,546 421,578 344 1 John Locke Max Scharrenbroich soc-Epinions1 75,879 508,837 152 2 Puneet Sharma soc-Slashdot0811 77,360 905,468 1578 3 Graphs from STANFORD’S SNAP Library soc-Slashdot0902 82,168 948,464 1781 3 (http://snap.stanford.edu/). Researchers who want to - create open tools - generate & host open data - support open scholarship Map, measure & understand social media Support tool projects to collection, analyze & visualize social media data. THANKS !!! to Microsoft External Research http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/ http://www.flickr.com/photos/amycgx/3119640267/ Location, Location, Location Network of connections among “ecomm” mentioning Twitter users ecomm Position, Position, Position • History: from the dawn of time! • Theory and method: 1934 -> • Jacob L. Moreno • http://en.wikipe dia.org/wiki/Jac ob_L._Moreno SNA 101 • Node A – “actor” on which relationships act; 1-mode versus 2-mode networks • Edge B – Relationship connecting nodes; can be directional C • Cohesive Sub-Group – Well-connected group; clique; cluster A B D E • Key Metrics – Centrality (group or individual measure) D • Number of direct connections that individuals have with others in the group (usually look at incoming connections only) • Measure at the individual node or group level E – Cohesion (group measure) • Ease with which a network can connect • Aggregate measure of shortest path between each node pair at network level reflects average distance – Density (group measure) • Robustness of the network • Number of connections that exist in the group out of 100% possible – Betweenness (individual measure) • # shortest paths between each node pair that a node is on • Measure at the individual node level F G • Node roles – Peripheral – below average centrality – Central connector – above average centrality C – Broker – above average betweenness H D I E http://en.wikipedia.org/wiki/Social_network • Central tenet • Social structure emerges from • the aggregate of relationships (ties) • among members of a population • Phenomena of interest • Emergence of cliques and clusters • from patterns of relationships • Centrality (core), periphery (isolates), • betweenness • Methods Source: Richards, W. (1986). The • Surveys, interviews, observations, NEGOPY network analysis log file analysis, computational program. Burnaby, BC: analysis of matrices Department of Communication, Simon Fraser University. pp.7-16 (Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)y http://en.wikipedia.org/wiki/Centrality • Degree • Closeness • Betweenness • Eigenvector Social Media Network Roles Welser, Howard T., Eric Gleave, Danyel Fisher, and Marc Smith. 2007. Visualizing the Signatures of Social Roles in Online Discussion
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